TY - JOUR

T1 - Time-Varying Efficiency and Predictability in Cryptocurrency Markets: Forward-Looking Dynamics

AU - Рогова, Елена Моисеевна

AU - Вукович, Дарко

AU - Зиновьев, Вячеслав Андреевич

AU - Shakib, Mohammed

AU - Hassan, Kabir M.

PY - 2025/8/11

Y1 - 2025/8/11

N2 - This study investigates the time-varying efficiency of major cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC) and XRP—within the framework of the adaptive market hypothesis (AMH). We introduce a forward-looking method by integrating predicted data into the Dominguez–Lobato (DL) and generalised spectral (GS) testing frameworks as part of Martingale Difference Theory (MDT). Our strategy allows us to forecast potential future inefficiencies in the market, advancing the traditional retrospective analyses (based on historical perspective) prevalent in the literature. We employ and test forecasted data to identify potential future shifts in market efficiency, as an extension of the Martingale difference hypothesis (MDH). The results indicate that cryptocurrency markets do not maintain a static level of efficiency but adapt over time, with varying degrees of predictability and inefficiency. The random forest (RF) model demonstrates the ability to forecast breaks in market efficiency.

AB - This study investigates the time-varying efficiency of major cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC) and XRP—within the framework of the adaptive market hypothesis (AMH). We introduce a forward-looking method by integrating predicted data into the Dominguez–Lobato (DL) and generalised spectral (GS) testing frameworks as part of Martingale Difference Theory (MDT). Our strategy allows us to forecast potential future inefficiencies in the market, advancing the traditional retrospective analyses (based on historical perspective) prevalent in the literature. We employ and test forecasted data to identify potential future shifts in market efficiency, as an extension of the Martingale difference hypothesis (MDH). The results indicate that cryptocurrency markets do not maintain a static level of efficiency but adapt over time, with varying degrees of predictability and inefficiency. The random forest (RF) model demonstrates the ability to forecast breaks in market efficiency.

KW - гипотеза адаптивного рынка

KW - криптовалютный рынок

KW - случайный лес

KW - случайное блуждание

KW - разность мартингалов

KW - adaptive market hypothesis

KW - cryptocurrencies

KW - martingale difference hypothesis

KW - random forest

KW - random walk

UR - https://onlinelibrary.wiley.com/doi/10.1002/ijfe.70039

UR - https://www.mendeley.com/catalogue/3c009051-3637-3aba-aab4-425a9d46f8fb/

U2 - 10.1002/ijfe.70039

DO - 10.1002/ijfe.70039

M3 - Article

JO - International Journal of Finance and Economics

JF - International Journal of Finance and Economics

SN - 1076-9307

M1 - 70039

ER -